Identification of the novel prognostic biomarker, MLLT11, reveals its relationship with immune checkpoint markers in glioma - Chen, Long; Xiong, Zujian; Zhao, Hongyu; Teng, Chubei; Liu, Hongwei; Huang, Qi; Wanggou, Siyi; Li, Xuejun (Frontiers in Oncology, 2022)
Aim This study aimed to explore the expression pattern of MLLT11 under different pathological features, evaluate its prognostic value for glioma patients, reveal the relationship between MLLT11 mRNA expression and immune cell infiltration in the tumor microenvironment (TME), and provide more evidence for the molecular diagnosis of glioma and immunotherapy. Methods Using large-scale bioinformatic approach and RNA sequencing (RNA-seq) data from public databases The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and The Gene Expression Omnibus (GEO)), we investigated the relationship between MLLT11 mRNA levels and pathologic characteristics. The distribution in the different subtypes was observed based on Verhaak bulk and Neftel single-cell classification. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used for bioinformatic analysis. Kaplan–Meier survival analysis and Cox regression analysis were used for survival analysis. Correlation analyses were performed between MLLT11 expression and 22 immune cells and immune checkpoints in the TME. Results We found that MLLT11 expression is decreased in high-grade glioma tissues; we further verified this result by RTPCR, Western blotting, and immunohistochemistry using our clinical samples. According to the Verhaak classification, high MLLT11 expression is mostly clustered in pro-neutral (PN) and neutral (NE) subtypes, while in the Neftel classification, MLLT11 mainly clustered in neural progenitor-like (NPC-like) neoplastic cells. Survival analysis revealed that low levels of MLLT11 expression are associated with a poorer prognosis; MLLT11 was identified as an independent prognostic factor in multivariate Cox regression analyses. Functional enrichment analyses of MLLT11 with correlated expression indicated that low MLLT11 expression is associated with the biological process related to the extracellular matrix, and the high expression group is related to the synaptic structure. Correlation analyses suggest that declined MLLT11 expression is associated with increased macrophage infiltration in glioma, especially M2 macrophage, and verified by RTPCR, Western blotting, and immunohistochemistry using our clinical glioma samples. MLLT11 had a highly negative correlation with immune checkpoint inhibitor (ICI) genes including PDCD1, PD-L1, TIM3(HAVCR2), and PD‐L2 (PDCD1LG2). Conclusion MLLT11 plays a crucial role in the progression of glioma and has the potential to be a new prognostic marker for glioma.